The Hasso Plattner Institute offers degree programs in “IT Systems Engineering” that are unique Germany-wide. It is a practical form of computer science study that places special emphasis on the conception and development of complex software systems. The education at Hasso Plattner Institute is tuition free.

Research in HPI’s topic areas is carried out in Potsdam and internationally at the Hasso Plattner Institute Research School. It is distinguished by its high scientific standard, practical approach and close cooperation with industry. The primary focus of HPI’s research is on highly complex IT systems.

Hasso Plattner Institute holds an unique position on the landscape of German universities. Research and teaching are concentrated in the area of IT systems engineering. The highly ranked Bachelor, Master and PhD degree programs have a high practical and innovation orientation. Design Thinking spans a bridge between Hasso Plattner Institute in Potsdam and Hasso Plattner Institute of Design in Stanford.

The Hasso Plattner Institute has educational programs for both high school students and working professionals. It operates its own IT learning platform - openHPI - which provides free online courses. The Youth Academy organizes computer science camps and events for high school students. Professionals can take advantage of educational opportunities in the field of Design Thinking at the HPI Academy.

Description

In many big data scenarios, data comes with high speed as a never-ending stream of events. For these data streams, decisions need to be made often on the fly. Unfortunately, common algorithms are rarely applicable in scenarios with streaming data. Most algorithms were designed for offline settings, i.e., the entire data set needs to be scanned and processed (multiple times), before a decision can be made. Therefore, novel algorithms on data streams are needed: In this seminar, we implement, evaluate (and at best improve) streaming algorithms from current research projects. We will look at data stream mining, recommendations for data streams, and algorithms for graph streams where edges and vertices arrive in a streaming fashion. Students will develop data streaming techniques and implement prototypes based on current research projects: Each team, consisting of two students, chooses and presents a challenging research task and implements the proposed solution using the streaming framework Apache Kafka with Kafka Streams . Students may select one of the papers listed here. This is a first selection of current research about data streams (tbc). We welcome further suggestions. ● Chaitanya Manapragada, Geoffrey I. Webb, and Mahsa Salehi, Extremely Fast Decision Tree , KDD 2018. See also https://github.com/chaitanya-m/kdd2018.git ● Kai Sheng Tai, Vatsal Sharan, Peter Bailis, and Gregory Valiant, Sketching Linear Classifiers over Data Streams , SIGMOD 2018. See also https://github.com/stanford-futuredata/wmsketch ● Yang Zhou, Tong Yang, Jie Jiang, Bin Cui, Minlan Yu, Xiaoming Li, and Steve Uhlig, Cold Filter: A Meta-Framework for Faster and More Accurate Stream Processing , SIGMOD 2018. See also https://github.com/streamclassifier/ColdFilter ● Aneesh Sharma, Jerry Jiang, Praveen Bommannavar, Brian Larso, and Jimmy Lin, GraphJet: Real-Time Content Recommendations at Twitter , VLDB 2016. See also https://github.com/twitter/GraphJet ● Xiangmin Zhou, Dong Qin, Xiaolu Lu, Lei Chen, and Yanchun Zhang, Online Social Media Recommendation over Streams , ICDE 2019. ● Dhivya Eswaran, Christos Faloutsos, Sudipto Guha, and Nina Mishra, SpotLight: Detecting Anomalies in Streaming Graphs . KDD 2018. This is a project seminar: There will be a few weekly lectures including an introductory lecture and an invited talk from industry about Stream Processing with Apache Kafka. Teams will frequently meet with the assigned supervisor.

Examination

In teams, with team size is two students, you will be completing the following tasks: ● Active participation during all seminar events. ● Short presentation of the selected research paper. ● Intermediate presentations demonstrating insights regarding your research prototype. ● Regular meetings with advisor. ● Implementation of a research prototype with Kafka and Kafka Streams. ● Final presentation demonstrating your solution. ● Code & documentation (on GitHub). The documentation should contain information on how to execute and evaluate your solution. Furthermore, it should also show strengths and weaknesses of the implementation.